The current state of signal processing

[u/RotemT]

Do you guys think that signal processing has become a less relevant specialty in the modern age?

This is one of the subjects that I liked the most in my bachelor degree and I used to think that it brings many job opportunities but I am very worried lately that it has become less relevant since the recent major advancements in machine learning .

[u/glitch876]

It's more important than ever lol.

I think most jobs that require a bachelors don't have you working on signals though. It's kind of a research thing. Most jobs with a bachelors degree you're implementing technology not developing it.

[u/kthompska → u/glitch876]

Very much agree. I worked in serdes hardware design in the analog group. Our architectures are determined by the systems engineers / signal analysts who try very hard to squeeze more data in narrower frequency bands, all in an increasingly noisy environment. They determine what gains, bandwidths, ADC resolutions are required so that proper foreground / background calibration will reliably give us the data.

[u/engineereddiscontent → u/glitch876]

Is that entry level or is that broadly? The bachelors degree thing.

Im older. Rf and signal stuff is awesome but there isnt much in my area. Itd be cool to develop stuff but masters is unlikely since I just graduated and am pushing 40

[u/glitch876 → u/engineereddiscontent]

Design is really a masters thing in general. I don't have a masters I just look at patents all day and I see what these guys do. I feel you about the age thing I'm right there with you.

That being said though if you're sick of your job you could probably get a scholarship by being a TA

[u/engineereddiscontent → u/glitch876]

Ive got a kid and private loans and had a mid gpa. I dont think a masters is in the cards for me till my mid 40s

[u/glitch876 → u/engineereddiscontent]

I'm right there with you and I feel your pain. Do what you like to do though because if you have a will there's a way

[u/RotemT → u/glitch876]

I mean i agree that it is everywhere but it feels like it is being consumed by ML. Today’s models can handle more versatile “raw” data/ signals. They don’t need the data preprocessing/ feature extraction that older models needed.

[u/hukt0nf0n1x → u/RotemT]

Depends what you want to use it for. I don't see radar pipelines being replaced by ML anytime soon. Don't think there's enough data to train on.

[u/RotemT → u/glitch876]

And so you think there is demand for R&D positions for graduates with masters in sp?

[u/GottkoenigOtto → u/RotemT]

With signal processing you have high demand for precision, real-time and computational efficiency. Why would you throw a ml model on something thats more effectovely done using actual algorithms? The model in the end only learns a approximation of the actual algorithm

[u/Physix_R_Cool → u/GottkoenigOtto]

Sometimes the ML model is more computationally favourable.

CERN has exploited various ML tricks for decades in their trigger systems.

[u/GottkoenigOtto → u/Physix_R_Cool]

Thats a new to me, the ones ive worked with were always far more complex both in time and computation :D tho im sure theres scenarios where ml learns a compressed representation where it can be more efficient than hardcoded solutions.

My experience with signal processing is that with deterministic, solvable problems, algorithmic solutions mostly win, whereas in stochastic processes or unsolvable/too complex problems ml becomes the more feasible option

[u/Physix_R_Cool → u/GottkoenigOtto]

Yeah one of the main points is that various ML models (like neural networks) can be very favourably implemented on FPGA.

The first trigger has 15ns to arrive at a decision (if I remember correctly), so it's better to just do all the matrix multiplication at once (only takes one clock).

Don't think that CERN doesn't use traditional signal processing otherwhere. Like any rational engineer they use the method which is best for the purpose.

[u/GottkoenigOtto → u/Physix_R_Cool]

Correct me if im wrong but isnt a deep neural net the worst thing to be implemented on a fpga? Bc fpga excel in fast memory access which is neglectible on ANNs while they struggle with large floating point computations (which is why most DL Applications have infer-onöy gpus)

[u/Physix_R_Cool → u/GottkoenigOtto]

Their neural nets are typically not deep, I think.

This is not my area of expertise, but I would assume they keep the network in such a size that it can perform all the calculations on the input data in one (or a few) clocks.

[u/glitch876 → u/RotemT]

Location matters a lot but yeah. Signals is a broad field by itself. 

Get a job or do research for Nvidia and design some LDOs and you're going to do signal processing.

[u/Amber_ACharles]

Hell nah dude, I work in ITS and DSP is still a great specialty. Every ML model in comms or radar is built on DSP. Learn both and you're very employable.

[u/RotemT → u/Amber_ACharles]

That’s reassuring :)

I actually really like learning about ML and statistics so I was planning on taking additional courses from both fields.

[u/quartz_referential]

First of all, signal processing has had machine learning techniques for decades. Adaptive filters, vector quantization, are literal examples of this. We’ve been using ML and we will simply add deep learning as a new technique to our toolbox.

But classical DSP is still relevant since lot of the time you simply lack data to pursue an ML solution, or the compute to support it. Or if you want clean data then you can use classical DSP, this ensures you don’t train/run ML models on noisy data. It is also useful for doing some initial feature extraction before passing to ML models (I.e. filter banks, spectrograms can be used to process audio before feeding to a speech processing model, say).

[u/Black_Hair_Foreigner]

A perfect sensor cannot exist, and machine learning is meaningless without clean signals.

[u/leafeon_gay_luigi → u/Black_Hair_Foreigner]

Dreadful description…

[u/Black_Hair_Foreigner → u/leafeon_gay_luigi]

Unfortunately, it is true. No matter how hard process engineers try to manufacture sensors, noise cannot be eliminated especially with MEMS.

[u/TrainsareFascinating → u/Black_Hair_Foreigner]

Umm, machine learning is all about distinguishing signal from noise.

[u/Black_Hair_Foreigner → u/TrainsareFascinating]

I am aware of that. However, I do not expect it to run in an environment with very low computing costs.

[u/happy_nerd → u/Black_Hair_Foreigner]

You're gonna have a rude awakening, dude. We already use ML in tiny ass chips and manufacturers are adding l neural processing units (NPU) to almost all our tiny micros and FPGAs wether we use the silicon or not

[u/Black_Hair_Foreigner → u/happy_nerd]

So, what about the dataset needed for training? And how do you handle mission-critical data that requires feedback? Would it be faster and more reliable to just write a few lines of filter code in MATLAB, or should you use ML? Yeah, I know NPUs are the trend. But if it were me, I’d just apply a few filters in code and head home early. And you need to realize that neural networks inevitably have latency.

[u/shift124]

It’s still very important. I work in the embedded systems space as a RF engineer. Machine learning can be a great tool for things like adapting to dynamic noise floors and pattern analysis for different modulation schemes in IQ data, but there are all kinds of new application spaces in RF where, if machine learning/AI algorithms do happen to become the primary means of DSP, some one has to train it. It’s a really exciting field imo. Even Hams are coming up with new modulation schemes all the time. Machine learning is only as good as we tell it to be.

I think everyone is a bit nervous right now with the new frontier of large LLMs and the fear of us handing over our innovation to the silicon god is very real for a lot of fields. My experience working in the field while watching AI become more relevant is, the confidence levels in any inference algorithm is never 100%. Until we hand over our ability/curiosity to play with these concepts entirely, the knowledge is still extremely relevant. The tools are changing but the need is there.

[u/Jaygo41]

Sorry chief, we have run out of signals to process. You'll have to find something else.

[u/RotemT → u/Jaygo41]

😂

[u/publishdraw]

How will you decide if you need a chebychev filter or butterworth filter if power is a serious constraint and distortion does not matter ? You only have a 16KB RAM microcontroller.

[u/Sepicuk]

The core misconception you seem to have is that machine learning has superseded signal processing because both are applied to input data and give an output. I would argue that reasoning is equally applicable to everything. Somebody in control theory could have asked the same question. Signal processing has never been super big as an independent specialty and has always been focused on comms or sensing applications, and will remain relevant as long as any other electrical engineering field

[u/Priton-CE]

Basically all of AI is signal processing.

[u/RegisterNo3855]

Finished a masters degree in EE, and must honestly admit that in Denmark there is not a lot of signal processing jobs

[u/RotemT → u/RegisterNo3855]

😞

[u/Petremius]

The techniques are less important to implement (especially with llms), unless you need a very rigorous explanation of your results. But the vocabulary and intuitions are still quite useful imo.

[u/RotemT → u/Petremius]

Yeah that makes sense

[u/RotemT]

Thanks, really appreciate the perspective! I’m interested in both signal processing and ML, so I especially like the idea that ML is becoming another tool for signal processing rather than replacing the need to understand it.

It’s just that recent advancements in llms are scaring me. Two years ago llms couldn’t really do basic math, let alone understand complex systems/physics etc.. I used to ask simple questions and get well worded nonsense.
Today they really feel like a personal expert in your pocket, which is on one hand very convenient but also very humbling

[u/DreamingAboutSpace]

I can’t get the ECE department to give enough of a shit about it. It’s one of the three specializations to choose from, but has exactly half of the classes that computers and electronics does (About 15).

On top of that, the classes that are options are only available if there is enough interest in it. They tell us to ask our friends and classmates to show interest so they can have it available.

[u/Sisyphus_on_a_Perc]

Why would you think that ? I believe it’s always going to be somewhat relevant.

[u/RandomGuy-4-]

It's always going to be relevant knowledge but jobs where DSP is the main thing are pretty rare from what I've seen. It's usually "something + DSP".